7 papers
On the Memorization Behavior of LLMs in Generative Recommendation: Observations, Implications, and Training Strategies
Sunwoo Kim, Sunkyung Lee, Clark Mingxuan Ju +5
Generative recommendation (GR) has emerged as a promising direction for recommender systems. Recently, large language models (LLMs) have been increasingly adopted for GR, as their…
From Relevance to Authority: Authority-aware Generative Retrieval in Web Search Engines
Sunkyung Lee, Jihye Back, Donghyeon Jeon +4
Generative information retrieval (GenIR) formulates the retrieval process as a text-to-text generation task, leveraging the vast knowledge of large language models. However, existi…
Enhancing Time Awareness in Generative Recommendation
Sunkyung Lee, Seongmin Park, Jonghyo Kim +2
Generative recommendation has emerged as a promising paradigm that formulates the recommendations into a text-to-text generation task, harnessing the vast knowledge of large langua…
GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion
Sunkyung Lee, Minjin Choi, Eunseong Choi +2
Generative recommendation is an emerging paradigm that leverages the extensive knowledge of large language models by formulating recommendations into a text-to-text generation task…
DIFF: Dual Side-Information Filtering and Fusion for Sequential Recommendation
Hye-young Kim, Minjin Choi, Sunkyung Lee +2
Side-information Integrated Sequential Recommendation (SISR) benefits from auxiliary item information to infer hidden user preferences, which is particularly effective for sparse i…
Linear Item-Item Model with Neural Knowledge for Session-based Recommendation
Minjin Choi, Sunkyung Lee, Seongmin Park +1
Session-based recommendation (SBR) aims to predict users' subsequent actions by modeling short-term interactions within sessions. Existing neural models primarily focus on capturin…